Course outlines

Published by ABS

Amsterdam MBA

20 April 2015

ANALYTICS COURSES

Statistics for Big Data

Lecturers: Professors Peter Boswijk & Cees Diks (UvA - Econometrics)

After this course, the student should be able to:

Identify Big Data problems that require statistical techniques;
Apply the statistical techniques correctly on Big Data problems;
Understand the properties of these techniques, and the role of assumptions;
Interpret the conclusions properly;
Program in “R”.

Econometrics

Lecturers: Professor Peter Boswijk (UvA – Econometrics)

After this course, the student should be able to:

Translate economics and business questions into econometric models and hypotheses;
Analyze discrete choice data, panel data, time series;
Interpret the conclusions properly, and understand the role of assumptions;
Use the statistical programming language R for econometric model building.

System Optimization

Lecturers: Professor Ger Koole (VU - Mathematics & Computer Science)

After this course, the student should be able to:

Optimize deterministic systems

Being able to model business problems as optimization problems;
Recognize (Mixed-Integer) Linear Programs (MILP's);
Use Excel and AIMMS to program and solve MILP’s;
Interpret the results.

Optimize stochastic systems

Understand the role of uncertainty in business problems;
Understand basic models for capacity planning and the role of uncertainty;
Develop simulation models using simulation software;
Interpret the results.

Quantitative Marketing

Lecturers: Dr. Maarten Soomer (ORTEC & UvA) & Dr. Kevin Pak (Sundio & UvA)

After this course, the student should be able to:

Analyze online behavior;
Implement Search Engine Advertisement (e.g. with Google AdWords);
Obtain customer insights from social media;
Apply Dynamic Pricing / Revenue Management;
Set-up customer targeting / real-time bidding.

BUSINESS COURSES

Big Data Strategy & Implementation

Lecturers: Martin Heijnsbroek (MIcompany) & Abdel Ichou (MIcompany)

After this course, the student should be able to:

Drive focus on the critical Big Data opportunities (goal);
Assess readiness on opportunity capture, metrics and models, technology, and people (situation);
Develop a coherent vision and road-map to capture (direction);
Lead a Big Data initiative to success (execute).

Financial Accounting

After this course, the student should be able to:

Understand the ‘language’ of business, its uses and limitations;
Interpret and understand the impact economic events have on the Balance Sheet, Income Statement and Statement of Cash Flows;
Understand and describe the measurement theories used in financial accounting;
Recognize how financial statements communicate economic events to third parties (i.e. owners, investors, creditors) and the impact this information has on them.

Law & Ethics for Big Data

Lecturers: Lieneke Viergever, Eva Visser & Jeroen Koëter (all from Project Moore)

After this course, the student should be able to:

Understand principles of privacy and data protection;
Identify the possible risks of Big Data for privacy;
Perform a law/ethics compliance scan;
Understand technologies to minimize privacy risks;
Design privacy-friendly systems and services;
Understand that proper communication and transparency is key.

Operations & Supply Chain Management

After this course, the student should be able to:

To understand O&SCM issues in general business context;
To understand the importance of O&SCM, as well as the need for an integrated vision on O&SCM in any organisation;
To understand the linkages of Operations and Supply Chain to other business areas;
To be able to use tools and techniques in O&SCM environments;
To identify, analyse and resolve typical problems that arise in managing Operations and Supply Chain;
To be able to understand and resolve O&SCM implementation issues.

Amsterdam Leadership Programme

After this course, the student should be able to:

Develop insights into personal strengths, weaknesses, core values, and development priorities;
Develop the ability to inquire and advocate in an effective way;
Reflect on the effectiveness of one's leadership behaviours by applying practical concepts;
Apply various leadership and engagement concepts in practice;
Create a culture of learning and giving/receiving high quality feedback;
Generate an effective team charter in order to maximise the impact of team work.

Corporate Finance

After this course, the student should be able to:

Have achieved a rigorous working knowledge of key issues in finance;
To understand the accepted theoretical foundations and conceptual underpinnings of finance and financial economics;
To apply concepts and ideas in practical business situations.

Entrepeneurship

After this course, the student should be able to:

To innovate and launch new products & services by executing techniques from the start-up and venture world
Have developed the knowledge and skills (individually and as a team) how to create a new offer that solves a real business need
Have a good understanding of what it takes to introduce and scale disruptive innovations both from a market strategy perspective as well as organizational perspective Special focus on what it takes to do these type of initiatives within a larger enterprise.

Competitive Strategy

After this course, the student should be able to:

Understand and explain how strategy, competition, value creation, and value capturing drive the performance of organizations;
Describe and summarize the main processes, theories and tools of competitive strategy;
Assess and discuss the main strengths and weaknesses of these processes, theories, and tools;
Apply the processes, theories, and tools in diagnosing strategy issues in case-based and real-life organizations;
Utilize the processes, theories, and tools in order to improve the strategy of case-based and real-life organizations

Valuation

After this course, the student should be able to:

To understand concepts of time value of money, arbitrage, CAPM and (N)PV and to be able to apply this to the evaluation of capital budgeting decisions and company valuation
To be able to reflect on the limitations of the NPV approach
To analyse, report and present business cases on valuation, capital budgeting and capital structure
To calculate the appropriate WACC for capital budgeting decisions

Consumer Behaviour

After this course, the student should be able to:

Understand core theories from related fields (e.g. psychology, behavioural economics) that are central to comprehending consumer behaviour;
Analyse how these theories are applied and adapted to fit the marketing/consumption context;
Evaluate academic research on consumer behaviour topics;
Apply consumer behavior concepts to real-life business cases related to organizations’ marketing strategies;
Present and discuss their analyses (formally and informally) in a manner that benefits fellow students' understanding and learning experience.

Human Resource Management

After this course, the student should be able to:

Describe, reproduce and critically evaluate the theoretical arguments underpinning (the strategic importance of human resources,
the strategic role of HRM and its added value, the role and effectiveness of teams in organizations and diversity and national culture);

Understand and analyse the debates and controversies in the literature on HRM that we will present and discuss during the course;
Apply these theories to firms by analysing business problems in cases and exercises.

Marketing Strategy

After this course, the student should be able to:

Develop awareness of the range, scope and complexity of the marketing strategy topics, issues and problems related to the process of marketing management.
Offer an integrated and process-oriented perspective on marketing and strategy at the business level.
Provide a theoretical and empirical ground for understanding the process of business innovation.
Translate new perspectives into action, through case discussions.

Financial Modelling

After this course, the student should be able to:

Apply the theory of finance in building implementable models, with Excel used as a programming vehicle;

Risk Management & Financial Institutions

After this course, the student should be able to:

Evaluate how risk management contributes to firm value;
Measure and manage a range of financial risks to which companies are exposed;
Understand recent issues on risk management (e.g. subprime crisis and lessons from risk management failures).

International Study Trip Silicon Valley

Lecturers: Professor Marc Salomon (UvA) & Albert Bogaard (ORTEC & UvA)

The Silicon Valley Study Trip is primarily:

To learn how the Silicon Valley - ECO system stimulates both Innovation andEntrepreneurship;
To understand what it takes to start a business in the US;
To understand the role of Universities, Accelerators, Incubators, Venture Studios, Venture Capital and large companies, such as Google, Paypal etc. in the Silicon Valley – ECO system.

COMPUTER SCIENCE COURSES

Machine Learning

Lecturers: Professor Marcel Worring (UvA - Computer Science & UvA - Business) & Professor Max Welling (UvA - Computer Science)

After this course, the student should be able to:

Understand methods from machine learning, in particular; (decision trees and decision forests, clustering and topic modeling, logistic regression and deep learning, matrix factorization and times series analysis & spatio-temporal event modeling);

Apply the methods in advanced techniques (text analytics, image and video analytics and recommendation);

Apply the techniques in large scale use-cases.

Information systems for Big Data

After this course, the student should be able to:

Understand the functionality and limitations of different databases (traditional relational databases, modern non-relational database models and databases designed for big data analytics);

Understand data management models (privacy, governance and security by design);

Employ databases in big data analytics.

Data visualisation

Lecturers: Dr. Willem van Hage (Synerscope & VU - Computer Science) & Professor Marcel Worring (UvA - Computer Science & UvA - Business)

After this course, the student should be able to:

Understand the purpose of various types of data visualisation, ranging from info-graphics to visual analytics
Apply design principles to design information dashboards
Understand the applicability of various visualisation techniques
Use visualization tools to perform visual analysis